A deep architecture that is able to run in real-time while providing accurate semantic segmentation.
The core of the architecture is a novel layer that uses residual connections and factorized convolutions in order to remain efficient while retaining remarkable accuracy.
pip3 install 'scipy' 'matplotlib' 'pycocotools' 'opencv-python' 'easydict' 'tqdm'
Go to visit COCO official website, then select the COCO dataset you want to download.
Take coco2017 dataset as an example, specify /path/to/coco2017
to your COCO path in later training process, the unzipped dataset path structure sholud look like:
coco2017
├── annotations
│ ├── instances_train2017.json
│ ├── instances_val2017.json
│ └── ...
├── train2017
│ ├── 000000000009.jpg
│ ├── 000000000025.jpg
│ └── ...
├── val2017
│ ├── 000000000139.jpg
│ ├── 000000000285.jpg
│ └── ...
├── train2017.txt
├── val2017.txt
└── ...
bash train_erfnet_dist.sh --data-path /path/to/coco2017/ --dataset coco
Ref: https://github.com/Eromera/erfnet_pytorch
Ref: torchvision
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